The purpose of the present training grant is to support the holding of the Complex Systems Summer School in Santa Fe, New Mexico. The goal of the Summer School is to provide an introduction to the study of "complex" behavior in mathematical, physical and living systems as part of an interdisciplinary effort to promote the understanding of complex systems. At each school, a set of lectures and seminars is provided by leading researchers in diverse fields unified by their use of complex systems analysis. The present training grant was awarded on the assumption that students in the neurosciences would benefit from exposure to a diverse set of scientists using dynamical systems methods in approaching their specific research interests. In order to carry out this mandate we make certain that a significant proportion of the students admitted to the School have an interest in neuroscience, biology and neural networks research. The school itself evolves in response to the experiences of the previous years, and evaluations provided by the students. We will continue to employ a feature designed to help students benefit from the entire range of topics - a set of tutorials at the start of the school to provide students with a basic "toolkit" in areas such as evolution, computation, dynamical systems theory and chaos, disordered systems, information theory, neural nets, and neuroscience. Two new features have been added in recent years: first, students are organized into research groups on a variety of topics in the first week. These groups meet regularly throughout the month, planning and carrying out computational projects when possible. In the final week groups make reports. Second, we have introduced some thematic structure to the choice of lecturers. Each week two related lecture series will be offered, with related seminars in the afternoons. Our long-term goals for the school are to provide a framework within which neurobiologists, and others, can learn from each other, and can benefit from methods and techniques pioneered in diverse fields of study of complexity.